# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.

import os
import os.path as osp
import re
import shutil
import traceback

import torch

from extensions.sd_dreambooth_extension.dreambooth.db_config import from_file
from extensions.sd_dreambooth_extension.dreambooth.utils import cleanup, printi, unload_system_models, \
    reload_system_models
from modules import shared

unet_conversion_map = [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

unet_conversion_map_resnet = [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

unet_conversion_map_layer = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
    # loop over downblocks/upblocks

    for j in range(2):
        # loop over resnets/attentions for downblocks
        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

        if i < 3:
            # no attention layers in down_blocks.3
            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

    for j in range(3):
        # loop over resnets/attentions for upblocks
        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

        if i > 0:
            # no attention layers in up_blocks.0
            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

    if i < 3:
        # no downsample in down_blocks.3
        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

        # no upsample in up_blocks.3
        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

hf_mid_atn_prefix = "mid_block.attentions.0."
sd_mid_atn_prefix = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    hf_mid_res_prefix = f"mid_block.resnets.{j}."
    sd_mid_res_prefix = f"middle_block.{2 * j}."
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def convert_unet_state_dict(unet_state_dict):
    # buyer beware: this is a *brittle* function,
    # and correct output requires that all of these pieces interact in
    # the exact order in which I have arranged them.
    mapping = {k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        mapping[hf_name] = sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                v = v.replace(hf_part, sd_part)
            mapping[k] = v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            v = v.replace(hf_part, sd_part)
        mapping[k] = v
    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

vae_conversion_map = [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
    # down_blocks have two resnets
    for j in range(2):
        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
        sd_down_prefix = f"encoder.down.{i}.block.{j}."
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
        sd_downsample_prefix = f"down.{i}.downsample."
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
        sd_upsample_prefix = f"up.{3 - i}.upsample."
        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

    # up_blocks have three resnets
    # also, up blocks in hf are numbered in reverse from sd
    for j in range(3):
        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
        sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
    hf_mid_res_prefix = f"mid_block.resnets.{i}."
    sd_mid_res_prefix = f"mid.block_{i + 1}."
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))

vae_conversion_map_attn = [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def reshape_weight_for_sd(w):
    # convert HF linear weights to SD conv2d weights
    return w.reshape(*w.shape, 1, 1)


def convert_vae_state_dict(vae_state_dict):
    mapping = {k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            v = v.replace(hf_part, sd_part)
        mapping[k] = v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                v = v.replace(hf_part, sd_part)
            mapping[k] = v
    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
    weights_to_convert = ["q", "k", "v", "proj_out"]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"mid.attn_1.{weight_name}.weight" in k:
                print(f"Reshaping {k} for SD format")
                new_state_dict[k] = reshape_weight_for_sd(v)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#



textenc_conversion_lst = [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight')
]
protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
textenc_pattern = re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
code2idx = {'q': 0, 'k': 1, 'v': 2}


def convert_text_enc_state_dict_v20(text_enc_dict: dict[str, torch.Tensor]):
    new_state_dict = {}
    capture_qkv_weight = {}
    capture_qkv_bias = {}
    for k, v in text_enc_dict.items():
        if k.endswith('.self_attn.q_proj.weight') or k.endswith('.self_attn.k_proj.weight') or k.endswith(
                '.self_attn.v_proj.weight'):
            k_pre = k[:-len('.q_proj.weight')]
            k_code = k[-len('q_proj.weight')]
            if k_pre not in capture_qkv_weight:
                capture_qkv_weight[k_pre] = [None, None, None]
            capture_qkv_weight[k_pre][code2idx[k_code]] = v
            continue

        if k.endswith('.self_attn.q_proj.bias') or k.endswith('.self_attn.k_proj.bias') or k.endswith(
                '.self_attn.v_proj.bias'):
            k_pre = k[:-len('.q_proj.bias')]
            k_code = k[-len('q_proj.bias')]
            if k_pre not in capture_qkv_bias:
                capture_qkv_bias[k_pre] = [None, None, None]
            capture_qkv_bias[k_pre][code2idx[k_code]] = v
            continue

        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
        #        if relabelled_key != k:
        #            print(f"{k} -> {relabelled_key}")

        new_state_dict[relabelled_key] = v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
        new_state_dict[relabelled_key + '.in_proj_weight'] = torch.cat(tensors)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
        new_state_dict[relabelled_key + '.in_proj_bias'] = torch.cat(tensors)

    return new_state_dict


def convert_text_enc_state_dict(text_enc_dict: dict[str, torch.Tensor]):
    return text_enc_dict


def compile_checkpoint(model_name: str, half: bool, use_subdir: bool = False, reload_models=True):
    """

    @param model_name: The model name to compile
    @param half: Use FP16 when compiling the model
    @param use_subdir: The model will be saved to a subdirectory of the checkpoints folder
    @param reload_models: Whether to reload the system list of checkpoints.
    @return: status: What happened, path: Checkpoint path
    """
    unload_system_models()
    shared.state.textinfo = "Compiling checkpoint."
    shared.state.job_no = 0
    shared.state.job_count = 6
    printi(f"Compiling checkpoint for {model_name}...")
    if not model_name:
        return "Select a model to compile.", "No model selected."

    ckpt_dir = shared.cmd_opts.ckpt_dir
    models_path = os.path.join(shared.models_path, "Stable-diffusion")
    if ckpt_dir is not None:
        models_path = ckpt_dir

    config = from_file(model_name)
    if "use_subdir" in config.__dict__:
        use_subdir = config["use_subdir"]

    v2 = config.v2
    total_steps = config.revision

    if use_subdir:
        os.makedirs(os.path.join(models_path, model_name), exist_ok=True)
        checkpoint_path = os.path.join(models_path, model_name, f"{model_name}_{total_steps}.ckpt")
    else:
        checkpoint_path = os.path.join(models_path, f"{model_name}_{total_steps}.ckpt")

    model_path = config.pretrained_model_name_or_path

    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
    try:
        printi("Converting unet...")
        # Convert the UNet model
        unet_state_dict = torch.load(unet_path, map_location="cpu")
        unet_state_dict = convert_unet_state_dict(unet_state_dict)
        # unet_state_dict = convert_unet_state_dict_to_sd(v2, unet_state_dict)
        unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
        printi("Converting vae...")
        # Convert the VAE model
        vae_state_dict = torch.load(vae_path, map_location="cpu")
        vae_state_dict = convert_vae_state_dict(vae_state_dict)
        vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
        printi("Converting text encoder...")
        # Convert the text encoder model
        text_enc_dict = torch.load(text_enc_path, map_location="cpu")

        # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
        is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

        if is_v20_model:
            print("Converting text enc dict for V2 model.")
            # Need to add the tag 'transformer' in advance, so we can knock it out from the final layer-norm
            text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
            text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
            text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
            if not config.v2:
                config.v2 = True
                config.save()
                v2 = True
        else:
            print("Converting text enc dict for V1 model.")
            text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
            text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
            if config.v2:
                config.v2 = False
                config.save()
                v2 = False

        # Put together new checkpoint
        state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
        if half:
            print("Halving model.")
            state_dict = {k: v.half() for k, v in state_dict.items()}

        state_dict = {"global_step": config.revision, "state_dict": state_dict}
        printi(f"Saving checkpoint to {checkpoint_path}...")
        torch.save(state_dict, checkpoint_path)
        if v2:
            cfg_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "configs", "v2-inference-v.yaml")
            cfg_dest = checkpoint_path.replace(".ckpt", ".yaml")
            print(f"Copying config file to {cfg_dest}")
            shutil.copyfile(cfg_file, cfg_dest)
    except Exception as e:
        print("Exception compiling checkpoint!")
        traceback.print_exc()
        return f"Exception compiling: {e}", ""

    try:
        del unet_state_dict
        del vae_state_dict
        del text_enc_path
        del state_dict
    except:
        pass
    cleanup()
    if reload_models:
        reload_system_models()
    return "Checkpoint compiled successfully.", "Checkpoint compiled successfully."
